Sleep classification from wrist-worn accelerometer data using random forests
Abstract Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolu...
Main Authors: | , , , , , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-01-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-020-79217-x |